Adaptive Enhanced Cell Identity Positioning

ABSTRACT

The present invention introduces methods and devices for provision of position determination assisting data as well as methods, devices and systems for performing position determinations based on this assisting data. The position determination assisting data comprises area definitions, each of which being related at least to a respective cell relation configuration ( 41 ). The cell relation configuration being determined at least by cell-IDs of cells fulfilling a specific radio condition criterion when received. Preferably, the cell relation configuration is also dependent on relative radio conditions between different cells and/or transmission mode. The area definitions are in particular embodiments polygons, which preferably are re-calculated successively, automatically and on-line. The recalculations are based on high-precision position measurements of opportunity, clustered ( 42 ) at least with respect to prevailing cell relation configuration for that user equipment performing the high-precision position measurements. Preferably, the area definitions are calculated with a predefined confidence level.

TECHNICAL FIELD

The present invention relates in general to methods and systems forposition determination of mobile terminals in a cellular communicationsnetwork, and in particular to such position determination involving cellareas.

BACKGROUND

All cellular communications systems are divided into cells, where UserEquipment (UE) served by one, or when in soft(er) handover several basestations. Each base station may serve UEs in more than one cell. Theimportant point from a positioning and navigation perspective is thatthe cell where a specific UE is located is known in the cellular system.Hence, after determination of the geographical area covered by aspecific cell; it can be stated that the UE is located somewhere withinsaid geographical area, as long as it is connected and the reported cellidentity of the serving cell is equal to the cell identity correspondingto the particular geographical area.

An example of positioning within a Wideband Code Division MultipleAccess (WCDMA) cellular system operates briefly as follows, assumingthat the positioning operates over the Radio Access Network ApplicationPart (RANAP) interface. The procedures are however similar for e.g. theGlobal System for Mobile communications (GSM) and Code Division MultipleAccess 2000 (CDMA 2000).

A message requesting a location estimate is received in the ServingRadio Network Controller (SRNC) over the RANAP interface. The quality ofservice parameters of the message is assumed to be such that the RadioNetwork Controller (RNC) selects the cell identity positioning method.The SRNC determines the serving cell identity of the UE to be positionedand retrieves a pre-stored polygon that represents the extension of theserving cell. The SRNC sends the resulting cell polygon back to the corenetwork over the RANAP interface, using a cell polygon format in alocation report message.

It should, however, be noted that due to the complexity of the radiopropagation, the cell polygon format is only an approximation of theextension of the true cell. The selection of the polygon format isdictated by the need to have a reasonably flexible geographicalrepresentation format, taking e.g. computation complexities andreporting bandwidths into account.

Since the polygon format approximates the cell extension, the polygon isnormally pre-determined in a cell-planning tool to represent the cellextension with a certain confidence. The confidence is intended torepresent the probability that the UE is located within the polygon,conditioned on the fact that it is connected to the cell that isrepresented by the cell polygon. The underlying off-line calculation ofthe cell polygon can e.g. be based on coverage simulations of varyinglevels of sophistication. However, the end result is normally not veryreliable when the confidence of the calculated cell extension isconsidered.

The accuracy of the cell identity positioning method is mainly limitedby the size of the cell, something that prevents it from being used inmore sophisticated navigation applications. Its main advantages includea very low response time as well as the fact that it is widely spreadand always available where there is cellular coverage. The cell identitymethod is also straightforward to implement and has no UE impact. Theadvantages has lead to an interest for the development of Enhanced cellidentity (E-cell ID) positioning methods that aim at enhancing theaccuracy of the basic cell identity method at the same time as theadvantages of the method are retained.

One principle for E-cell ID positioning aims at combining the cellextension model with a distance measure. Two possibilities towards thisend are Round Trip Time (RTT) measurements and path loss measurements.The most accurate of these two alternatives is the RTT measurement. Thepath loss measurement suffers from shadow fading effects, which resultin accuracies that are of the order of half the distance to the UE. Inthe RTT measurement principle, the travel time of radio waves from theRadio Base Station (RBS) to the UE and back is measured. The RTT methodalone defines a circle around the RBS. By combining this informationwith the cell polygon, left and right angles of the circle can becomputed.

Another idea for enhanced cell identity positioning has been to usepre-calculated maps of the regions where the UE is in soft(er) handoverwith one or several cells. Such areas are significantly smaller than thewhole cell opening up for a better accuracy of the determined position.Normally these maps are pre-calculated in the planning tool, exactly asthe ordinary cell polygons.

In some situations high-precision positioning is required. In thepresent disclosure, “high-precision positioning methods” are defined todenote positioning methods that have a potential to meet theNorth-American E-911 emergency positioning requirements. Methods thatmeet these requirements are capable of obtaining positioning accuraciesof:

-   -   either (terminal based) 50 meters (67%) and 150 m (95%),    -   or (network based) 100 meters (67%) and 300 m (95%).

Assisted Global Positioning System (A-GPS) positioning is an enhancementof the Global Positioning System (GPS). GPS reference receivers attachedto e.g. a cellular communication system collect assistance data that,when transmitted to GPS receivers in terminals connected to the cellularcommunication system, enhance the performance of the GPS terminalreceivers. Typically, A-GPS accuracy can become as good as 10 meters.Additional assistance data is collected from the cellular communicationsystem directly, typically to obtain a rough initial estimate of theposition of the terminal together with a corresponding uncertainty ofthe initial estimate. This position is often given by a cell identitypositioning step.

The Uplink Tune Difference Of Arrival (UTDOA) positioning method isbased on time of arrival measurements performed in several RBSs oftransmissions from the UEs. The signal strengths are higher than inA-GPS, something that enhances the ability to perform positioningindoors. The accuracy of UTDOA is expected to be somewhat worse thanthat of A-GPS though, mainly because the radio propagation conditionsare worse along the surface of the earth than when GPS radio signals arereceived from satellites at high elevation angles.

SUMMARY

A general problem with existing positioning methods based on cell-ID isthat the accuracy of the determined positions is low. The confidencevalue is normally not determined with the best possible accuracy, withrespect to the calculated cell area.

A general object of the present invention is thus to provide formethods, devices and systems giving possibilities for improved positiondetermination accuracy. A further object is to provide for methods anddevices providing positioning assisting data allowing for positiondeterminations of a higher accuracy. Yet a further object of the presentinvention is to provide for methods, devices and systems operating withsmaller distinguishable areas. It is also a further object of thepresent invention is to provide for methods, devices and systems whichprovides defined areas having a well established confidence value.

The above objects are achieved by methods, devices and systems accordingto the enclosed patent claims. In general words, the present inventionintroduces a method for provision of position determination assistingdata. The position determination assisting data comprises areadefinitions, each of which being related to a respective cell relationconfiguration. The cell relation configuration being determined at leaston cell-IDs of cells, in which signals to/from a user equipment to bepositioned fulfil a specific radio condition criterion. Preferably, thecell relation configuration is also dependent on relative radioconditions between different cells and/or transmission modes. The areadefinitions are in particular embodiments polygons, which preferably arere-calculated successively, automatically and on-line. Therecalculations are preferably based on high-precision positionmeasurements of opportunity, clustered with respect to prevailing cellrelation configuration for that user equipment performing thehigh-precision position measurements. Preferably, the area definitionsare calculated with a predefined confidence level. The specific radiocondition corresponds in a particular embodiment to radio conditionsdefining the active set of cells, i.e. cells that are in soft(er)handover with the user equipment. The specific radio conditioncorresponds in another particular embodiment to radio conditionsallowing for measurements on signals, e.g. radio conditions defining thedetected set of cells.

The position determination assisting data is preferably used todetermine a position of a user equipment. A cell relation configurationfor the user equipment to be positioned is determined and the relatedarea definition can be used as an approximation of the user equipmentposition. The area definition obtained in this manner can also beutilized as refined prior position information for e.g. A-GPS or UTDOApositioning, and to refine Rll positioning.

The present invention also provides devices and systems for carrying outthe methods described above. All functionality of the invention is in atypical embodiment located in a positioning node, e.g. a RNC, a SAS(Stand Alone SMLC (Serving Mobile Location Centre)) node, a support nodefor configuring and monitoring of the system, or in a completely standalone node. However, it is also possible to have different partsimplemented in different nodes communicating with each other.

Among the numerous advantages of the present invention can be mentionedthe following: A database of area definitions for cell relationconfigurations are built up adaptively and automatically. In preferredembodiments, the area of the area definitions, typically a cell polygon,is minimized, for a specific value of the confidence. This maximizes theaccuracy of the cell identity positioning method. The confidence iseasily determined accurately. The performance of the UTDOA and A-GPSpositioning methods can be improved by initial positioning data obtainedby the present invention. The area definition information isautomatically refined, a fact that is useful e.g. when parts of theRadio Network (RAN) is re-planned.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention, together with further objects and advantages thereof, maybest be understood by making reference to the following descriptiontaken together with the accompanying drawings, in which:

FIG. 1 is an illustration of a cellular communications system;

FIGS. 2A-E are illustrations of examples of division of a cell intosmaller areas according to coverage from neighbouring cell signals;

FIGS. 3A-C are illustrations of examples of cell relationconfigurations;

FIG. 4A is a flow diagram of the main steps of an embodiment of a methodaccording to the present invention;

FIG. 4B is a flow diagram of the main steps of another embodiment of amethod according to the present invention;

FIG. 4C is a flow diagram of the main steps of yet another embodiment ofa method according to the present invention;

FIG. 4D is a flow diagram of the steps of an embodiment of step 212 ofFIGS. 4A-D;

FIG. 5 is an example of a cell polygon;

FIG. 6 is an illustration of a 3GPP polygon message information element;

FIG. 7 is an in illustration of an initial geometry for a shrinkingpolygon method;

FIG. 8 is an illustration of the geometry used for determining a maximumpolygon corner movement;

FIG. 9 is an illustration of the geometry for calculation of the areareduction;

FIG. 10A is an illustration of an initiation of a numerical example ofpolygon computation;

FIG. 10B is an illustration of the result of the polygon shrinkingalgorithm applied on FIG. 10A;

FIG. 11 is a block diagram of the main parts of an embodiment of a nodeaccording to the present invention;

FIG. 12 is an illustration of RTT measurements; and

FIG. 13 is an illustration of A-GPS measurements.

DETAILED DESCRIPTION

In the present disclosure “position determination assisting data” isused to define data that is used in cell-related activities in cellularcommunications system, such as radio network planning or positioningbased on cell-ID. In particular, it may refer to the cell relationconfiguration and related area definitions used in the presentdisclosure. This should not be mistaken for “assistance data”, which inthe present disclosure is used solely in A-GPS discussions.

In the present disclosure, WCDMA systems are used as a model system.However, anyone skilled in the art realizes that the basic principles ofthe present invention are applicable to any cellular communicationsystem. The invention is thus not limited to the exemplifyingembodiments as such.

FIG. 1 illustrates a general WCDMA system 100. Radio base stations 30(RBS) are spread over the coverage area of the system and servesantennas 20, which in this embodiment are sectorized antennas. A cell 15is associated with each sector of the antennas 20, as the area in whichconnection to the communications system preferably is performed throughthat particular sector. The RBSs 30 are connected to a Radio NetworkController (RNC) node 40, which in a typical case comprises apositioning node 45. The UEs 10 and the RNC 40 communicates over theso-called RRC (Radio Resource Control) interface 37 that is transparentto the RBS 30. The RBSs 30 and the RNC 40 are nodes comprised in theUTRAN (Universal Mobile Telecommunication System Radio Access Network)35. The RNC 40 is further connected to the Core Network (CN) 50 of thecommunications system 100 via a RANAP (Radio Access Network ApplicationPart) interface 47.

A user equipment (UE) 10 is situated in the area covered by the cellularcommunications system 100. The user equipment communicates with the ownradio base station 30 through signals 25. However, also signals 26 fromand to neighbouring RBSs 30 may be possible to detect. If theneighbouring signals 26 are strong enough for supporting actualcommunication, the corresponding cell could be included in a so-calledactive set of cells which participates in soft(er) handover. By softhandover is meant the case where two different non-colocated RBSs areused, whereas softer handover refers to one RBS with several sectors. Aspecial case is when the UE is connected to two sectors of the same RBS,i.e. softer handover. However, for the purpose of the present invention,there is no substantial difference between soft and softer handover andboth cases can be handled analogously. The signal 26 may in some casesbe too weak to be included in the active set, but strong enough to allowfor identification of the transmitting RBS. Such signals may e.g. beused for positioning purposes. Finally, neighbouring signals 26 may alsobe too weak to enable any use at all.

When a UE 10 is connected to a certain RBS via certain radio links, theUE 10 is likely to be situated within the associated cell. The cellarea, in WCDMA defined by a polygon that describes the cell extension,is normally not determined with the best possible accuracy, with respectto the true extension of the cell. The approximate cell area istypically determined in connection with cell planning and may notcorrespond perfectly to the real situation. Normally, the actualconfidence level of the cell area extension is not specified.Furthermore, radio conditions may also be altered after the cellplanning has been preformed. It would therefore be advantageous to tunethe confidence and the pre-calculated cell polygon for each cell, usingfield data. This can normally not be afforded though, in particularsince the radio conditions may change with time. The present inventiondisclosure reveals a way to obtain such tuning automatically.

FIG. 2A illustrates a cell 15, with a UE 10 connected. For simplicity inthe coming explanations, the RBS is in this case assumed to be placed atthe centre of the cell, a so-called omni-cell configuration. When the UE10 is connected to the RBS, it can with a certain probability bedetermined to be present within the cell 15.

However, as mentioned briefly above, the UE may also be within radiorange from other RBSs as well. In FIG. 2B, borders 12 of areas withinwhich signals to/from a neighbouring RBS are strong enough to allow forsoft(er) handover are indicated. In this oversimplified model, theborders 12 are drawn as circles, having their centre at a neighbouringRBS. It is easily seen that the borders 12 divide the cell 15 intosmaller areas 11, 11A, 11B, 11Z. In the area 11Z, only signals from theown RBS 30 are useful. However, in e.g. area 11A, signals to/from oneneighbouring RBS are also useful for soft(er) handover purposes and arethus included in the so-called active set of cells. In area 11B, signalsto/from two neighbouring cells are strong enough and the active set thencomprises two neighbouring cells. It can now easily be understood, thatthe content of the active set can be used for positioning purposes. Byconsulting the active set list, it can be determined in which of thepart areas 11, 11A, 11B, 11Z, the UE 10 is likely to be situated.

However, most often, soft(er) handover information is not used forpositioning purposes, probably since it is likely to be difficult tocompute with a sufficient accuracy. According to the present invention,area definitions that describe any soft(er) handover regions are useful.In WCDMA, such area definitions can conveniently be polygon definitions.However, using prior art cell planning principles would normally notprovide area definitions determined with the best possible accuracy,with respect to the true extension of any soft(er) handover regions.Furthermore, the confidence value of any soft(er) handover regions wouldnormally, using prior art methods, not be determined with the bestpossible accuracy, with respect to any calculated soft(er) handoverarea. It would therefore be advantageous to tune the confidence and thepre-calculated cell polygon for each cell, using field data. This cannormally not be afforded though, in particular since the radioconditions may change with time, even more than for the basic cell.However, the present invention reveals a way to obtain such tuningautomatically.

Signals from neighbouring RBSs can be utilized further. As mentionedabove, even if the signals to and from neighbouring RBSs are not strongenough for allowing soft(er) handover, they may still be strong enoughto enable determination of the identity of the transmitting RBS/UE.Corresponding set of cells is typically referred to as the detected setof cells. Also this information can be used for positioning purposes. InFIG. 2C, the cell 15 is once again illustrated. Now, not only borders 12for soft(er) handover (of which only one is denoted by a referencenumber) are illustrated, but also borders 13 of areas in which theidentity of the transmitting RBS or UE can be obtained in downlink oruplink, respectively, e.g. corresponding to the detected set of cells.The cell 15 is thereby further divided in even smaller part areas 11,11C-G, 11Z. For instance, in area 11E, signals from one neighbouring RBSare, besides the signals from the own RBS, used for soft(er) handover,while signals from another neighbouring RBS only are used foridentifying the transmitting RBS.

If not only the existence of signals of certain strengths is considered,but also the relative strengths as compared to other signals, an evenfiner division of the original cell can be achieved. In FIG. 2D, thepart areas that involves signals from more than one neighbouring RBS aredivided according to which signal that is the strongest. Areas 11H-K arethereby possible to define.

As mentioned above, the real situation is, however, not so ideal as theexamples of FIGS. 2A-D may indicate. Instead, the borders 12, 13 are noteasily determined and are typically non-circular. FIG. 2E illustrates asituation that could correspond to a real situation. Anyone skilled inthe art, then realises that any theoretical pre-determination of theareas 11, 11A-K, 11Z, is impossible in practice.

According to the present invention, two types of information areconnected to each other in order to achieve the advantages of thepresent invention; cell relation configuration and high-precisionpositioning data.

The first type of information is a cell relation configuration. Thiscell relation configuration corresponds to the divisions in the previousexamples of FIG. 2A-E. The cell relation configuration comprises in abasic embodiment data representing the “own” cell as well as anyneighbouring cell, in which the RBS corresponding theretotransmits/receives detectable signals to/from the user equipment inquestion which full a certain criterion. In a typical view, the cellrelation configuration can be considered as a list of cell identitiescorresponding to signals fulfilling a specific radio condition criterionwith respect to a certain UE. FIG. 3A illustrates an embodiment of sucha list. The first row corresponds to the own cell. The cell ID is “ID1”.The UE can in this example also communicate with cells “ID2”, “ID3”,“ID4”, “ID5”. Each combination of cells will in this embodiment define aparticular cell relation configuration.

FIG. 3B illustrates another embodiment of a cell relation configuration.Here, the relative signal strengths are taken into account, and thecells are thereby sorted in strength order. A signal to/from cell “ID3”is thereby stronger than signals to/from e.g. cells “ID5”. This meansthat a cell relation configuration in this embodiment is not onlydependent on which cells that are comprised in the list, but also inwhich order. There may even be a difference in strength order betweenuplink and downlink, which also can be utilised in defining areas.

Also other signal-strength related quantities can be utilised fordefining the cell relation configuration, e.g. path loss andsignal-to-interference ratio.

FIG. 3C illustrates another embodiment of a cell relation configuration.Here, the signal strengths are also classified. It can be seen that cell“ID1” is classified as “the own cell”, and cells “ID3” and “ID5” areclassified to be comprised in the active set of cells, i.e. they areutilised for soft(er) handover purposes. This means that a cell relationconfiguration in this embodiment is not only dependent on which cellsthat are comprised in the list and in which order, but also on theclassification of the cells.

In the view of the above examples, anyone skilled in the art realizesthat a cell relation configuration is easily obtainable for any UE thatis situated within a coverage area of a cellular communications network.

The second type of necessary data is as mentioned further abovehigh-precision positioning data. This can be derived in any possibleway. UTDOA and A-GPS are mentioned earlier in the background, but othermethods can be useful as well. The inventive idea is to collectrelations between high-precision positioning data and cell relationconfiguration for the corresponding UE at the positioning instant. Thisis preferably performed by using measurements of opportunity, i.e. highprecision measurements that would anyway be performed for some otherreason. Alternatively, the measurements could be arranged on purpose.For instance, e.g. for the purpose of improved radio network planning,high-precision position measurement devices could be spread over acertain area in a planned manner. Positions are determined as well ascell relation configurations. Another alternative could be to regularlyorder user equipment capable of high-precision positioning to providesuch measurements. For each possible cell relation configuration (i.e.in a simple view set of ordered cell identities), a measurement list isthen setup. All high-precision measurements that are related to aspecific cell relation configuration are then collected in one specificlist of high-precision measurements. In other words, the high-precisionpositioning data are clustered dependent on the prevailing cell relationconfiguration. The measurements of one such list thus form a cluster ofmeasurements that can be expected to be located in a specificgeographical area. The clustering of results of the high-precisionposition determinations thus gives a number of separate clusteredresults. When a suitable number of high-precision positioning datapoints are clustered in one of the separate clustered results, it ispossible to define an area which contains a pre-determined fraction ofthe high-precision positioning data points. It can then be concludedthat a UE having a certain cell relation configuration is situatedwithin the defined area with a confidence level corresponding to thepre-determined fraction.

In other words, a UE that not by itself has any high-precisionpositioning capabilities may utilise previous high-precision positioningof other UEs for achieving an improved accuracy in positiondetermination.

It can be noticed that the achieved area definitions can be considerablydifferent from the actual radio coverage. The reason is that areashaving good radio conditions but never hosting any user equipments willtend to be excluded from the determined area. The associated area willinstead be an area based on a combination of radio coverage propertiesand probability for user equipment occurrence.

The ideas of the present invention can also be illustrated by a flowdiagram of the main steps of an embodiment of a method according to thepresent invention, illustrated in FIG. 4A. The procedure starts in step200. The procedure first comes to a section 202 for providing positiondetermination assisting data. This section starts with a step 204, inwhich a cell relation configuration for a particular UE is determined.The signals are typically registered and reported according to standardcellular communication system procedures and compiled to cell relationconfiguration. In step 206, a high-precision positioning of the UE isperformed, using any suitable high-precision positioning method. In step208, the high-precision positioning data is clustered dependent on thedetermined cell relation configuration. The steps 204 to 208 arerepeated a number of times, as indicated by the arrow 210.

When an appropriate number of measurement points are available for acertain cell relation configuration, the procedure may continue to step212, in which an area is determined, which resembles the spatialdistribution of the high-precision positioning data. Preferably, an areaas small as possible is computed, which still contains a pre-determinedfraction of the high-precision positioning data. In other embodiments,one may be satisfied with a fairly small area, even if the area is notthe absolute mathematical minimum. A relation between a certain cellrelation configuration and an area definition is thereby achieved. Iffurther data is added by the steps 204-208, the step 212 may also haveto be repeated as indicated by arrow 214. In particular, if the radioconditions are changing, permanently or for a longer period of time, thearea definitions have to be re-calculated and adapted to the newsituation. Each high-precision position measurement is then alsopreferably time stamped in order to make it possible to discardhigh-precision position measurements that are too old, and successivelyperforming new area optimizations.

The time stamping can also be utilised in systems where the distributionof user equipments is likely to differ considerably between differenttimes. For instance, if an office complex and a residence area arecomprised close to each other, it is e.g. more likely to find the userequipments in the residence area during the nights. Such variations canbe dealt with by discarding high-precision positioning data having arecording time of the day, of the week or of the year, that isconsiderably different from the present time. In other words, theclustering can be performed by only selecting measurements fulfilling acertain additional criterion. The area definitions can thereby be madetime dependent.

The selection criterion for the clustering can also be made on otherparameters. The Radio Access Bearer (RAB) could e.g. be one selectionparameter. The coverage for different RABs can differ considerably, andthe borders between different part areas can thereby change theirposition considerably. For instance, traffic transmitted by a 64 kbpslink may have a completely different coverage area than traffictransmitted by a 384 kbps link. By also clustering the measurements e.g.with respect to the used RAB, will enable an improved positioning, sincethe area to be determined is unique for the actual RAB used.

The information about the RAB is a type of auxiliary information aboutcircumstances of signalling that makes the selection criterion more areaselective. In a general approach, other auxiliary information can alsobe utilised in an analogue manner. Similarly, there are also auxiliarymeasurements of signalling properties that can be performed and used asa part of the selection criterion. An example is e.g. auxiliary RTTmeasurements, which is discussed further below. The selection criterioncan be thought of as an augmentation of the cell relation configuration.

The step 212 can be performed for one particular cell relationconfiguration, a group of cell relation configurations or all cellrelation configurations as well as for different clustering selectioncriteria.

The lists of measurements are preferably organized hierarchically sothat lists at higher levels can be constructed from lower levels in casethe number of measurements at lower (more detailed) level would beinsufficient for a reliable computation of a cell polygon.

When a UE is going to be positioned, the procedure enters into thesection 216 for position determination. This section starts with a step218, in which a cell relation configuration for the UE to be positionedis determined. This is typically performed in an analogue manner as instep 204. In step 220, the relation between a certain cell relationconfiguration and an area definition is used to provide an area in whichthe UE to be positioned is situated with a certain confidence. Thisconfidence level corresponds to the pre-determined fraction used duringthe area optimization. The procedure ends in step 299. The accuracy ofthe positioning may in the best cases be enough for e.g. theNorth-American E-911 emergency positioning requirements. However,positions achieved in this manner should not be used to improve the areadefinitions according to the section 202.

The timing of the different steps can be somewhat differing. In FIG. 4B,a flow diagram of another embodiment of a method according to thepresent invention is illustrated. Here the two sections 202 and 216 areinterleaved with each other. The step of optimising the area 212 is heretriggered by the step of determining the cell relation configuration218. The optimising step 212 is then preferably performed just for thecell relation configuration that was determined in step 218, in order tosave time. If the relations are determined in advance, i.e. before theactual positioning request occurs, as in FIG. 4A, the positioning can beperformed with a shorter delay. The embodiment of FIG. 4B insteadensures that the latest available data always is utilized.

The position determined in step 220 can constitute the finalpositioning, or it can constitute assistance data for a refinedpositioning. This is illustrated in FIG. 4C. Here an extra step 222 isincluded, where the position as achieved from the relation of step 220is utilised in a further positioning method in order to refine thepositioning further. Such further positioning methods can e.g. be RTTpositioning or A-GPS positioning, which are discussed further below.

The step of optimising the area 212 can be considered as one of the moreimportant parts of the present invention. In FIG. 4D, a presentlypreferred embodiment of this step is described more in detail. In step230, all the high-precision measurement points, n_(TOT), for the cellrelation configuration in question are encompassed by an area border.n_(TOT) is subsequently used as the inputted number of high-precisionmeasurement points in the first iteration of the following step. In step232, it is checked if the ratio (n_(k)−n)/n_(TOT) is larger or equal toa predetermined fraction R, where n is the number of high-precisionmeasurement points that is intended to be removed during the nextiteration of the routine. If the ratio is large enough, the areareduction can proceed at least one step further, and the procedurecontinues to step 234. In step 234, the area is reduced according to acertain pre-determined action plan to exclude n of the inputtedhigh-precision measurement points, leaving n_(k)−n remaining points,which is set as the new inputted number of high-precision measurementpoints for the next iteration. Preferably, step 234 is performed in sucha way that the area is minimized or at least reduced. The processreturns to step 232 again, which is illustrated by the arrow 236. If theratio in step 232 becomes smaller than R, the process is interrupted,since one iteration more would cause the ration to fall below R, and thearea is subsequently used as the area associated with the cell relationconfiguration in question.

In several systems, among these the WCDMA (Wideband Code DivisionMultiple Access) system, the preferred representation of thegeographical extension of the cell is given by a cell polygon format.The extension of a cell is described by 3-15 corners of a closed polygonwhich does not intersect itself. The format is two-dimensional and thecorners are determined as pairs of longitudes and latitudes in the WGS84geographical reference system. An example is illustrated in FIG. 5.There, an example of a cell polygon 89 with corners 90 is illustrated.The RBS (Radio Base Station) is typically located close to one of thecorners 90 of the cell polygon 89 said RBS serves. 3GPP systems providefor a messaging format for cell polygons. FIG. 6 illustrate the used3GPP Polygon message IE (Information Element). This IE is present in theLOCATION REPORT message that is returned to the core network over theRANAP interface after a successful cell identity positioning.

When the present invention is used as cell-ID positioning method, are-calculated polygon, rather than the pre-calculated polygon, thatcorresponds to the specific identity of the cell is reported over RANAPor Iupc (a logical interface between a RNC and a SAS within the UTRAN).Note that since the re-calculated polygons are consistent with thereporting format, the invention fits directly into the existingpositioning interfaces.

If the present invention is used as enhanced cell identity positioning,making use of soft(er) handover active sets or detectable cell sets, asimilar reporting can take place. In case there is a re-calculatedpolygon stored for the determined cell relation configuration, then there-calculated polygon is selected and reported over RANAP or Iupc.Again, the invention fits directly into the existing positioninginterfaces.

The area definition data should be organized so that it can beefficiently addressed using cell relation configuration information. Inthis way, fallback areas covering replacement regions, can be foundwhenever areas for certain regions have not been computed. Note thatthis situation may occur, e.g., because of insufficient measurementstatistics.

For instance, in case no polygon is computed for the specific cellrelation configuration, then the hierarchical structure of the storedcell relations and area definitions is exploited in some way. Onealternative is to disregard the last cell identity of the cell relationconfiguration and look for the re-calculated polygon for the so reducedcell relation configuration. In case there is a re-calculated polygonfor this reduced cell relation configuration, then this polygon isreported over RANAP or Iupc. In case there is still no polygon computedthen the second last cell identity of the cell relation configuration isremoved and the procedure repeated. This procedure can continue up totop level, where the cell relation configuration corresponds to theserving cell. In case there would still not be a re-calculated polygon,the pre-calculated polygon can be used. It should be noted that thereare many alternative strategies that are possible here.

Presently preferred embodiments for optimizing polygons are presented indetail in Appendix A. Briefly, one embodiment is simply focused onminimizing the total cell area around the clustered results whilemaintain a constraint of the confidence value. A non-linear optimizationproblem can be formulated and solved for this procedure.

Another embodiment is directed to a simple method for successivelyshrinking the cell area. The method is initiated by encompassing theclustered results associated with the cell relation configuration(s) inquestion by a polygon. The shrinking procedure is then based on alteringthe position of one corner of the polygon at a time along a firstpredetermined path according to predetermined routines or rules.Typically, these rules allow for exclusion of a predetermined number ofhigh-precision position determinations from the interior of theshrinking polygon. Preferably, the corner capable of giving the bestimprovement according to a predetermined criterion is selected to bemoved in each step. The predetermined criterion can e.g. be an as largearea reduction as possible. The predetermined criterion canalternatively be an as large distance reduction as possible between thecentre of gravity of all high-precision position determinations withinthe area and a polygon corner. In particular, the corner selection canbe decided by making tentative alterations of each corner and check whatimprovements on the predetermined criterion they will cause. This corneraltering step is then repeated until only a predetermined percentage ofthe high-precision position determinations of the cluster remains withinthe polygon. The first predetermined path is typically a curve throughthe original corner position and the centre of gravity for the clusteredhigh-precision positions. In the simplest form, the curve is a straightline through the original corner position and a centre of gravity.

In a particular embodiment of the present invention, the altering of thepolygon corner allows one of the high-precision position determinationsto be placed outside the polygon, but not two of the high-precisionposition determinations. This typically brings one of the clusteredhigh-precision position determinations to be placed on or in thevicinity of a connection line between the altered corner and aneighbouring corner. In a more elaborate embodiment, the altering cancomprise alternative predetermined paths, and the optimum choice amongtentative alterations along all these alternatives can be selected.

FIG. 11 is a block diagram of an embodiment of a positioning node 45 andrelated functionality according to the present invention. In the presentembodiment, which is assumed to be comprised in a WCDMA system, suchfunctionality is preferably comprised in the RNC 40. Another possibilityis to implement the invention in the SAS node (e.g. an Ericsson SMLC) onthe other side of the Iupc interface 47. Still another possibility is tolog measurements and perform the algorithms in OSS-RC or even acompletely external node. New interfaces and/or information elements inexisting interfaces allowing for exchange of detected cell sets andmeasured high-precision position determination results may then benecessary.

In the case the position determination assisting data, i.e. therelations between the cell relation configurations and the associatedareas are produced in an external node, the information has to beprovided to a positioning node in order to assist in positiondetermination procedures. The position determination assisting data canthen preferably be stored at a computer readable medium, and supplied tothe positioning node in a suitable manner, e.g. by downloading thecontent over a communication link or simply by providing a data memorydevice having the data stored therein.

The RNC 40 communicates with UEs, transparently via RBSs, using the RRCinterface 37. In the present context, at least two information types areof interest; positioning measurements 38, in particular high-precisionpositioning measurements, and neighbouring cell signal measurements 39,e.g. handover measurements. The neighbouring cell signal measurements 39are provided to cell relation configuration determining section 41,determining the cell relation configuration. In a particular embodiment,the cell relation configuration determining section 41 can be based on aprior-art active set functionality. The determined cell relationconfiguration of a particular user equipment is provided to a clusteringsection 42.

The positioning measurements 38 are provided to the positioning node 45.The high-precision positioning measurements are provided to ahigh-precision positioning section 46, which e.g. can comprise UTDOA orA-GPS based positioning. Other positioning measurements, e.g. cell ID orRTT positioning measurements are in the present embodiment provided to amedium-precision positioning section 48. The outcome of the analysis ofthe high-precision positioning measurements, i.e. high-precisionpositions are provided to the clustering section 42, where thehigh-precision position is associated with a corresponding cell relationconfiguration. The measurements are clustered depending on the cellrelation configuration and in particular embodiments also on otherselection criteria such that auxiliary information and/or auxiliarymeasurements, in particular recording time, utilised RAB and/or RTTmeasurements. RTT measurements could then, e.g., be provided by themedium-precision positioning section 48 as indicated by the broken arrow53. Auxiliary information, such as time or utilised RAB, and otherauxiliary measurements can be provided by an auxiliary informationsection 54. This auxiliary information section 54 can be arranged toprovide the information internally in the node and/or be arranged toachieve the information from outside.

The clusters of positions for a certain cell relation configuration andin some embodiments selected within a specific time interval or using aspecific RAB are provided to an algorithmic block 43. In the algorithmicblock 43, area definitions are calculated. One important objective ofthe present invention, to compute an area that describes each cluster ofmeasurements, at a specified confidence level, is performed in thealgorithmic block 43. In the WCDMA case, the preferred area definitionis a polygon defined by 3 to 15 corner coordinates. In a particularembodiment, the algorithmic block 43 provides polygons such that theprobability that a given fraction of high-precision measurements of acluster are located in the interior of the polygon. This algorithmicblock 43 preferably performs repeated re-calculations of polygons, forall measurement clusters with a sufficient number of recent enoughhigh-precision measurements. The area definitions are provided to anarea storage 44, where polygons representing a hierarchically organizedset of cell relation configurations are stored. The stored polygons arethen used by positioning algorithms of the system. The data structure ofthe stored polygons preferably contains a list of pointers covering eachrelevant cell relation configuration. Each such pointer points to acorresponding 3-15 corner polygon, computed repeatedly as describedabove. The data structure preferably also contains a time tag for eachpolygon that defines the time when the polygon was computed.

When a position, determination according to the principles of thepresent invention is requested, a cell relation configuration isdetermined in the cell relation configuration determining section 41 asusual. The result is forwarded to a control section 49 in thepositioning node 45. When a positioning request 51 is received, e.g. aso-called Location Reporting Control message over the RANAP interface47, the control section 49 may, based on quality of service parametersand UE capability, request a position determination by retrieving anarea definition from the area storage 44, which corresponds to thepresent cell relation configuration of the UE. The achieved areadefinition, preferably a polygon definition is included in a positioningreporting message 52, which typically is sent back over the RANAPinterface 47 using e.g. a so-called Location Report message. As in thephase of creating the position determination assisting data, auxiliaryinformation, such as time or utilised RAB, and other auxiliarymeasurements can also be used to refine the selection of the areadefinition. Such data is achieved by the auxiliary information section54.

If the area definitions are to be used together with any additionalpositioning method, the retrieved area from the area storage 44 isprovided to the high-precision positioning section 46 or themedium-precision positioning section 48, depending on the method to beused. The final determined position is then provided to the controlsection 49 for further reporting.

Most functionalities of the cell relation configuration determiningsection 41, the high-precision positioning section 46, themedium-precision positioning section 48 and the control section 49 aretypically available in prior art systems. However, connections creatingrelations between the cell relation configuration determining section 41on one side and the high-precision positioning section 46, themedium-precision positioning section 48 and the control section 49 onthe other side are previously unknown. Furthermore, the clusteringsection 42, the algorithmic block 43, the area storage 44 as well asconnections thereto are entirely novel. So is also functionality in thecell relation configuration determining section 41, the high precisionpositioning section 46, the medium-precision positioning section 48 andthe control section 49 needed for communicating with these novelfunctionalities.

One principle for enhanced cell identity positioning aims at combiningthe cell extension model (the area definition) with a distance measure.Two possibilities towards this end are round trip time measurementsand/or path loss measurements. The more accurate of these twoalternatives is the round trip time measurement. The path lossmeasurement suffers from shadow fading effects, which result inaccuracies that are of the order of half the distance to the UE. Theround trip time measurement principle is depicted in FIG. 12. Briefly,the travel time of radio waves from the RBS antenna 20 to the UE 10 andback is measured. The distance r from RBS antenna 20 to UE 10 thenfollows from the formula:

${r = {c\frac{T_{RTT}}{2}}},$

where T_(RTT) is the round trip time and where c is the speed of light.

The round trip time measurement alone defines a circle, or if theinaccuracy is accounted for, a circular strip 70 around the RBS antenna20. By combining this information with the cell 15 polygon, left andright angles of the circular strip 70 can be computed. When an areadefinition 11 according to the basic principles of the present inventionis available, the section 71 of the circular strip 70 on which the UEcan be situated can be further decreased, which is evident from FIG. 12.

A combination between the basic principles of the present invention andRTT measurements can also be obtained in an alternative way. In such anembodiment, RTT measurements can be quantified and used as an additionalparameter for the selection criterion for the clustering according tothe present invention. The use then becomes analogous with the selectionbased on different RABs. The procedures according to the presentinvention then are used for building areas corresponding to differentRTT measurement results. In practice, despite its appeared complexity;this may even be advantageous, since the real radio signal propagationoften can be significantly different from theoretical evaluations,making the circular description of FIG. 12 only a rough approximation.In GSM applications, TA measurements corresponding to coarse RTTmeasurements, could be utilised.

Also A-GPS performance can be further enhanced by the present invention.FIG. 13 illustrates a typical A-GPS system. A UE 10 receives GPS rangingsignals 81 from a number of space vehicles 80. A reference GPS receiver86 has knowledge about e.g. synchronisation of the space vehicles 80 andprovides assistance data 85 over a reference receiver interface 84 to aGPS interface 83 of the RNC 40. Orders for position measurements andassistance data 82 are provided over a RRC interface 37 to the UE 10. Bymeasuring the arrival times of the different GPS ranging signals 81, theUE is able to determine a high-precision position based also on theassistance data. A report of the determined position is sent back to theRNC 40. The assistance data used for making this position determinationinvolves among other data also an approximate initial position of the UE10. The more accurate this initial position is, the more sensitive thedetection of the GPS ranging signals can be made. This may in turn leadto a more accurate final position, or a final position of an equalaccuracy provided within a shorter time or by means of less demandingprocessing.

If the high-precision positions also include altitude data, i.e. theposition defines lateral position as well as height; the “areadefinitions” can be calculated as surfaces having a three-dimensionalextension. A positioning based on such position determination assistingdata will then result in a position also defining some kind of altitudeestimate. It is then possible e.g. to report the centre point of thecell polygon, augmented with altitude, as a 3D-point over RANAP. Thealtitude of a polygon corner can also be estimated, e.g. as a mean valueof some high-precision measurements in the vicinity of the corner inquestion.

The embodiments described above are to be understood as a fewillustrative examples of the present invention. It will be understood bythose skilled in the art that various modifications, combinations andchanges may be made to the embodiments without departing from the scopeof the present invention. In particular, different part solutions in thedifferent embodiments can be combined in other configurations, wheretechnically possible. The scope of the present invention is, however,defined by the appended claims.

APPENDIX A

The main parts of the presently preferred embodiment of the presentinvention are described in detail in this appendix.

Clustering

In this particular embodiment, it is assumed that the cell relationconfiguration is based on the active list of cells, i.e. cells active insoft handover. Corresponding modelling is possible also for othercluster selection rules.

The high-precision position measurements are typically obtainedexpressed in the WGS 84 geographical reference system. The measurementsthat are available at time t are denoted

(lat_(j)(t _(j))long_(j)(t _(j)))^(T) , j=1, . . . , N(t),  (1)

where lat_(j)(t_(j)) and long_(j)(t_(j)) denote the measured latitudeand longitude, respectively, at the time t_(i). N(t) denotes the totalnumber of available measurements at time t. ( )^(T) denotesmatrix/vector transpose.

At the same time t_(j) (to within some reasonable accuracy in time), thecell relation configuration is sampled for cell identities. The resultis the row vector (or pointer)

Configuration(t _(j))=(cID ₁(t _(j))cID ₂(t _(j)) . . . cID _(N)(t _(l)₎(t _(j))),  (2)

where cID_(l)(t_(j)) is the cell identity of the l:th strongest cell ine.g. softer handover, for the UE for which high-precision positioningwas performed at time t_(j). N(t_(j)) is the number of cells in the cellrelation configuration at time t_(j).

An arbitrary possible pointer used for clustering of measurements,defined according to (2), is now denoted by

Pointer_(k)=(Index₁(k) . . . . Index_(N(k))(k)), k=1, . . . , K  (3)

where Index_(l)(k) is the l:th component of the (fix) pointer k, N(k) isthe dimension of the pointer k and K is the number of counters. Thecorresponding list of high-precision position measurements is denoted byList_(k). At time t:

$\begin{matrix}{{{{List}_{k}(t)} = \begin{pmatrix}{{lat}_{k,1}\left( t_{k,1} \right)} & {{lat}_{k,2}\left( t_{k,2} \right)} & \ldots & {{lat}_{k,{M{({k,t})}}}\left( t_{k,{M{({k,t})}}} \right)} \\{{long}_{k,1}\left( t_{k,1} \right)} & {{long}_{k,2}\left( t_{k,2} \right)} & \ldots & {{long}_{k,{M{({k,t})}}}\left( t_{k,{M{({k,t})}}} \right)} \\{lat}_{k,1} & t_{k,2} & \ldots & t_{k,{M{({k,t})}}}\end{pmatrix}},} & (4)\end{matrix}$

where M(k,t) denotes the number of high-precision measurements of list kat time t. As stated above, measurements that are older than apre-specified threshold are discarded from each list. The maximum sizeof a list can also be pre-specified, in which case the oldestmeasurement is discarded irrespective of its age when a new measurementarrives.

When a new high-precision measurement and corresponding cell relationconfiguration is obtained at time t_(N(t)+1) the clustering algorithmoperates as follows:

For k = 1 to K If Pointer_(k) = Configuration(t_(N(k)+1))${{List}_{k}\left( t_{{N{(k)}} + 1} \right)} = \left( {{{List}_{k}(t)}\begin{pmatrix}{{lat}_{{N{(t)}} + 1}\left( t_{{N{(t)}} + 1} \right)} \\{{long}_{{N{(t)}} + 1}\left( t_{{N{(t)}} + 1} \right)} \\t_{{N{(t)}} + 1}\end{pmatrix}} \right)$ end else do nothing end end

Polygon Computation Notation

In order to facilitate an effective algorithmic description, thefollowing notation is needed:

p=(p₁ . . . p_(N))—one specific pointer, corresponding to a specificcell relation configuration.

r_(i,ll) ^(p)=(x_(i,ll) ^(p) y_(i,ll) ^(p))^(T), i=1, . . . , N_(p)—thepolygon corners corresponding to the cell relation configuration p inWGS 84 latitude longitude notation.

r_(i) ^(p)=(x_(i) ^(p) y_(i) ^(p))^(T), i=1, . . . , N_(p)—the polygoncorners corresponding to the cell relation configuration p in a localearth tangential Cartesian coordinate system, with the origin somewherein the coverage area of the cellular system. Coordinate axes are usuallyeast and north, disregarding the altitude.

r_(j,ll) ^(m,p)=(x_(j,ll) ^(m,p) y_(j,ll) ^(m,p)), j=1, . . . , N_(p)^(m)—the high-precision measurements used in order to determine thecorners of the polygon corresponding to the cell relation configurationp. Note that this measurements corresponds to one of the entries ofList_(k) that corresponds to p.

r_(j) ^(m,p)=(x_(j) ^(m,p) y_(j) ^(m,p)), j=1, . . . , N_(p) ^(m)—thehigh-precision measurements used in order to determine the corners ofthe polygon corresponding to the cell relation configuration p. Thehigh-precision measurements are transformed to the same local earthtangential Cartesian coordinate system, with the origin somewhere in thecoverage area of the cellular system, which is used above.

C^(p)—The specified confidence of the polygon corresponding to p. Thisvalue corresponds to the probability that the UE is located within thepolygon, when the cell relation configuration corresponds to P.

A^(p)—The area of the polygon corresponding to p.

P^(p)—The region defined by the polygon.

Coordinate Transformations

The procedure starts by a transformation of all high-precisionmeasurements corresponding to p to the local earth tangential Cartesiancoordinate system, in which all computations are performed. Only the newmeasurements, which have not already been transformed need to beprocessed.

Constrained Cell Area Minimization Problem

The principle behind the computation of the polygon is governed by thefollowing three ideas.

The area of the polygon should be as small as possible, therebymaximizing the accuracy.

The constraint of the confidence value should be maintained, for thehigh-precision measurements available.

Basic geometrical constraints on the polygon should be maintained, inparticular the requirement that the polygon should not be allowed tointersect itself, and that the last numbered corner point is connectedto the first (closeness).

The following minimization problem can then be set up for thecomputation of the corners of the polygon:

$\begin{matrix}{\left\{ {{\hat{r}}_{1}^{p},\ldots,{\hat{r}}_{N_{p}}^{p}} \right\} = {\underset{r_{1}^{p},\ldots,r_{N_{p}}^{p}}{{argmin}\;}{A^{p}\left( {r_{1}^{p},\ldots,r_{N_{p}}^{p}} \right)}}} & \left( {5a} \right)\end{matrix}$

subject to polygonal geometric constraints and (5b)

$\begin{matrix}{{\sum\limits_{\underset{r_{j}^{m,p} \in P^{p}}{j = 1}}^{N_{p}^{m}}\; 1} \geq {C^{p}{N_{p}^{m}.}}} & \left( {5c} \right)\end{matrix}$

This is a nonlinear optimization problem. Many methods that may beapplicable to the solution of (5a-c), have been developed over theyears.

In the following, a new algorithm is disclosed, that instead is based ona direct approach, adapted to the problem at hand. Note that this methodmay not solve (5a-c) exactly, however, it is based on the same ideas as(5a-c) but in a stepwise manner.

Shrinking Polygon Algorithm

The main idea of this algorithm is to start with an initial polygon thatcontains all the high-precision measurements collected for theparticular cell relation configuration. The initial polygon can e.g. becalculated from the centre of gravity of the high-precisionmeasurements, followed by a calculation of the maximum distance fromthis centre of gravity, for all high-precision measurements. Thisdefines a circle that contains all high-precision measurement points.The initial polygon is then selected to contain this circle.

Following this initial step, the area of the polygon is then reduced insteps, by movement of one selected corner point of the polygon inwardstowards the momentary centre of gravity, so that one high-precisionmeasurement point is eliminated from the interior of the polygon, foreach step. The area reduction is performed so that the area reduction,at each step, is maximized over all corner points, at the same time asthe constraints are maintained fulfilled.

Centre of Gravity

Since the high-precision measurements are treated as points(non-stochastic), the centre of gravity is the arithmetic mean, i.e.

$\begin{matrix}{{r_{CG} = {\begin{pmatrix}x_{CG} & y_{CG}\end{pmatrix} = {\frac{1}{N_{p}^{m,{rem}}}{\sum\limits_{q = 1}^{N_{p}^{m,{rem}}}\; \begin{pmatrix}x_{q}^{m,p,{rem}} & y_{q}^{m,p,{rem}}\end{pmatrix}^{T}}}}},} & (6)\end{matrix}$

where the superscript ^(rem) indicates high-precision measurements thathave not yet been removed from the interior of the shrinking polygon bythe shrinking polygon algorithm.

Initiation

Since the initiation of the algorithm only affects the N_(p) first stepsof the algorithm, a conservative approach is taken here. The first stepis to compute the maximum distance from the centre of gravity, i.e.

$\begin{matrix}{j_{\max}^{p} = {\max\limits_{j}\sqrt{\left( {x_{j}^{m,p} - x_{CG}} \right)^{2} + \left( {y_{j}^{m,p} - y_{CG}} \right)^{2}}}} & (7) \\{r^{p} = {\sqrt{\left( {x_{j}^{m,p} - x_{CG}} \right)^{2} + \left( {y_{j}^{m,p} - y_{CG}} \right)^{2}}.}} & (8)\end{matrix}$

Hence all high-precision measurements are now within a distance r^(p) ofthe centre of gravity. Note that if a finite number of polygon cornerpoints would be spread out around this circle, there is no guaranteethat the polygon contains all high-precision measurement points.

Since initial points, symmetrically spread around a circle, isattractive, an additional outer circle is determined, such that itcontains the simplest polygon with three corners that contains thecircle with radius r^(p), see FIG. 7. The initial polygon corner pointscan then be spread out around this outer circle with radius R^(p). It isgeometrically obvious that the largest outer circle is obtained for apolygon defined by the minimum amount of corners, 3.

The outer radius can now be related to the computed inner radius byconsideration of FIG. 7. Geometrical symmetry shows that

$\begin{matrix}{R^{p} = {\frac{r^{p}}{\sin (30)} = {2{r^{p}.}}}} & (9)\end{matrix}$

The initial polygon corner points {r_(i) ^(p,0)}_(i=1) ^(N) ^(p) canthen be distributed around the outer circle according to

$\begin{matrix}{x_{i}^{p,0} = {x_{CG} + {R^{p}{\cos \left( {360\frac{\left( {i - 1} \right)}{N_{p}}} \right)}}}} & (10) \\{y_{i}^{p,0} = {y_{CG} + {R^{p}{{\sin \left( {360\frac{\left( {i - 1} \right)}{N_{p}}} \right)}.}}}} & (11)\end{matrix}$

Other strategies are of course also possible.

Maximum Corner Movement

Note that the computations described in this subsection considerhigh-precision measurement points the remains in the interior of theshrinking polygon, at each iteration step. This is true for (12)-(21)and for (24)-(26), see below.

Movement with Respect to High-Precision Measurement Points

In order to assess which polygon corner that is most beneficial to moveinwards at a given iteration step, it is first necessary to determinewhat the maximum movement is. This needs to take two constraints intoaccount.

The second high-precision point that leaves the polygon when a specificcorner point is moved inward along the specified direction towards thecentre of gravity constrains the movement inwards. This requires asearch over all high-precision measurement points that remain inside thepolygon at the specific iteration step of the algorithm.

The first polygon line segment that is intersected when a specificcorner point is moved inward along the specified direction towards thecentre of gravity constrains the move inwards. This requires a searchover all line segments (between polygon corner points) of the polygon.

Both these constraints need to be checked. Together they determine theinward maximum movement.

The maximum polygon corner movement with respect to a specifichigh-precision measurement point can be determined as follows, referringto FIG. 8. That figure shows a situation with three adjacent polygoncorners r_(k) ^(p), r_(i) ^(p), r_(i) ^(p). The arbitrary numbering isdue to the need to cover up for the fact that the last and the first ofthe polygon corner points are connected.

The middle point r_(i) ^(p) is then moved inwards towards the centre ofgravity, i.e. into the interior 93 of the polygon. As a consequence theline segments 92 that connect r_(k) ^(p) and r_(i) ^(p), as well asr_(i) ^(p) and r_(i) ^(p) also move. At some point of the movement theconsidered high-precision measurement point may be intersected by eitherof these two line segments—both needs to be checked.

In order to determine a tentative point of intersection the movement ofr_(i) ^(p) is computed to be

r _(i) ^(p)(α^(p))=r _(i) ^(p)+α^(p)(r _(CG) −r _(i) ^(p))  (12)

Here α^(p) is a scalar parameter that varies between 0 and 1 when r_(i)^(p)(α) moves between r_(i) ^(p) and r_(CG). Note that this is astandard way to describe a line segment mathematically. Note also thatmovement may in this case extend beyond the centre of gravity.

A necessary (but not sufficient) requirement for an intersection of themoving boundary of the polygon with the considered high-precisionmeasurement point, is that r_(i) ^(p)(α^(p))−r_(k) ^(p) and r_(j)^(m,p)−r_(k) ^(p) become parallel, or that r_(i) ^(p)(α^(p))−r_(l) ^(p)and r_(j) ^(m,p)−r_(l) ^(p) become parallel. Exploiting the fact thatthe cross product between parallel vectors is zero, allows for acomputation of α^(p). Straightforward algebra gives the results:

$\begin{matrix}{\alpha_{ik}^{j,p} = \frac{{{- \left( {x_{i}^{p} - x_{k}^{p}} \right)}\left( {y_{j}^{m,p} - y_{k}^{p}} \right)} + {\left( {x_{j}^{m,p} - x_{k}^{p}} \right)\left( {y_{i}^{p} - y_{k}^{p}} \right)}}{{\left( {x_{CG} - x_{l}^{p}} \right)\left( {y_{j}^{m,p} - y_{k}^{p}} \right)} - {\left( {x_{j}^{m,p} - x_{k}^{p}} \right)\left( {y_{CG} - y_{i}^{p}} \right)}}} & (13) \\{\alpha_{il}^{j,p} = {\frac{{{- \left( {x_{i}^{p} - x_{l}^{p}} \right)}\left( {y_{j}^{m,p} - y_{l}^{p}} \right)} + {\left( {x_{j}^{m,p} - x_{l}^{p}} \right)\left( {y_{i}^{p} - y_{l}^{p}} \right)}}{{\left( {x_{CG} - x_{i}^{p}} \right)\left( {y_{j}^{m,p} - y_{l}^{p}} \right)} - {\left( {x_{j}^{m,p} - x_{l}^{p}} \right)\left( {y_{CG} - y_{i}^{p}} \right)}}.}} & (14)\end{matrix}$

The subscripts indicate the polygon corner points that define the linesegment under evaluation. The superscript denotes the index of thehigh-precision measurement point. Both (13) and (14) are candidates forbeing an active constraint. Note however, that a requirement for this isthat

α_(ik) ^(j,p)>0  (15)

α_(il) ^(j,p)>0  (16)

In case (15) and (16) do not hold, the corresponding intersectionstrategy needs to be discarded.

Assuming that (15) and (16) hold, it remains to check if theintersection point falls between the points that limit the line segmentof the polygon. This means that the following equations need to befulfilled, for some β_(ik) ^(j,p)∈[0,1] or β_(il) ^(j,p)∈[0,1]:

r _(j) ^(m,p) =r _(i) ^(p)(α_(ik) ^(j,p))+β_(ik) ^(j,p)(r _(k) ^(p) −r_(i) ^(p))  (17)

r _(j) ^(m,p) =r _(l) ^(p)(α_(il) ^(j,p))+β_(il) ^(j,p)(r _(l) ^(p) −r_(i) ^(p)).  (18)

Since the vectors leading to (13) and (14) are parallel, it is enough toconsider one of the coordinates of (17) and (18) when solving for β^(p).The results are:

$\begin{matrix}{\beta_{ik}^{j,p} = \frac{x_{j}^{m,p} - {x_{i}^{p}\left( \alpha_{ik}^{j,p} \right)}}{x_{k}^{p} - {x_{i}^{p}\left( \alpha_{ik}^{j,p} \right)}}} & (19) \\{\beta_{il}^{j,p} = {\frac{x_{j}^{m,p} - {x_{i}^{p}\left( \alpha_{il}^{j,p} \right)}}{x_{l}^{p} - {x_{i}^{p}\left( \alpha_{il}^{j,p} \right)}}.}} & (20)\end{matrix}$

The final logic needed in the evaluation of the point r_(j) ^(m,p), withrespect to the movement of r_(i) ^(p), can be briefly summarized asfollows. Provided that:

α_(ik) ^(j,p)>0 and 0<β_(ik) ^(j,p)<1, α_(ik) ^(j,p) represents afeasible maximum movement for the line segment between r_(i) ^(p) andr_(k) ^(p).

α_(ik) ^(j,p)>0 and β_(ik) ^(j,p)>1vβ_(ik) ^(j,p)<0, α_(ik) ^(j,p)represents an inward point but the feasible maximum movement is notrelevant since the intersection is outside the line segment betweenr_(i) ^(p) and r_(k) ^(p). In this case the inward movement shall notlimit the minimum allowed inward movement. This is accomplished bysetting α_(ik) ^(j,p)=α_(max), where α_(max) is a large inward movement,say 10.

α_(ik) ^(j,p)<0 and 0<β_(ik) ^(j,p)<1, α_(lk) ^(j,p) represents afeasible maximum movement for the line segment between r_(i) ^(p) andr_(k) ^(p). However, since it is an outward movement, it shall be set tozero since the algorithm is designed for inward movement.

α_(il) ^(j,p)0 and 0>β_(il) ^(j,p)>1, α_(il) ^(j,p) represents afeasible maximum movement for the line segment between r_(l) ^(p) andr_(l) ^(p).

α_(il) ^(j,p)>0 and β_(il) ^(j,p)>1vβ_(il) ^(j,p)<0, α_(il) ^(j,p)represents an inward point but the feasible maximum movement is notrelevant since the intersection is outside the line segment betweenr_(i) ^(p) and r_(k) ^(p). In this case the inward movement shall notlimit the minimum allowed inward movement. This is accomplished bysetting α_(il) ^(j,p)=α_(max), where α_(max) is a large inward movement,say 10.

α_(il) ^(j,p)<0 and 0<β_(il) ^(j,p)<1, α_(il) ^(j,p) represents afeasible maximum movement for the line segment between r_(i) ^(p) andr_(k) ^(p). However, since it is an outward movement, it shall be set tozero since the algorithm is designed for inward movement.

In case both α_(ik) ^(j,p) and α_(il) ^(j,p) are feasible maximummovements, the smallest one is chosen. The considered cases can besummed as follows:

$\begin{matrix}{\alpha_{i}^{j,p}\left\{ {\begin{matrix}{\alpha_{\max},} \\\alpha_{il}^{j,p} \\\alpha_{\max} \\\alpha_{il}^{j,p} \\\alpha_{\max} \\\alpha_{\max} \\\alpha_{ik}^{j,p} \\\alpha_{ik}^{j,p} \\\alpha_{\max} \\\alpha_{il}^{j,p} \\\alpha_{ik}^{j,p} \\{\min \left( {\alpha_{ik}^{j,p},\alpha_{il}^{j,p}} \right)} \\0\end{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}{{\alpha_{ik}^{j,p} < 0},} \\{{\alpha_{ik}^{j,p} < 0},}\end{matrix} \\{{\alpha_{ik}^{j,p} < 0},}\end{matrix} \\{{\alpha_{ik}^{j,p} < 0},}\end{matrix} \\{{\alpha_{ik}^{j,p} > 0},}\end{matrix} \\{{\alpha_{ik}^{j,p} > 0},}\end{matrix} \\{{\alpha_{ik}^{j,p} > 0},}\end{matrix} \\{{\alpha_{ik}^{j,p} > 0},}\end{matrix} \\{{\alpha_{ik}^{j,p} > 0},}\end{matrix} \\{{\alpha_{ik}^{j,p} > 0},}\end{matrix} \\{{\alpha_{ik}^{j,p} > 0},}\end{matrix} \\{{\alpha_{ik}^{j,p} > 0},}\end{matrix} \\{otherwise}\end{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}{{\alpha_{il}^{j,p} > 0},} \\{{\alpha_{il}^{j,p} > 0},}\end{matrix} \\{{\alpha_{il}^{j,p} > 0},}\end{matrix} \\{{\alpha_{il}^{j,p} > 0},}\end{matrix} \\{{\alpha_{il}^{j,p} < 0},}\end{matrix} \\{{\alpha_{il}^{j,p} < 0},}\end{matrix} \\{{\alpha_{il}^{j,p} < 0},}\end{matrix} \\{{\alpha_{il}^{j,p} < 0},}\end{matrix} \\{{\alpha_{il}^{j,p} > 0},}\end{matrix} \\{{\alpha_{il}^{j,p} > 0},}\end{matrix} \\{{\alpha_{il}^{j,p} > 0},}\end{matrix} \\{{\alpha_{il}^{j,p} > 0},}\end{matrix} \\\;\end{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}{{\beta_{ik}^{j,p} \notin \left\lbrack {0,1} \right\rbrack},} \\{{\beta_{ik}^{j,p} \notin \left\lbrack {0,1} \right\rbrack},}\end{matrix} \\{{\beta_{ik}^{j,p} \in \left\lbrack {0,1} \right\rbrack},}\end{matrix} \\{{\beta_{ik}^{j,p} \in \left\lbrack {0,1} \right\rbrack},}\end{matrix} \\{{\beta_{ik}^{j,p} \notin \left\lbrack {0,1} \right\rbrack},}\end{matrix} \\{{\beta_{ik}^{j,p} \notin \left\lbrack {0,1} \right\rbrack},}\end{matrix} \\{{\beta_{ik}^{j,p} \in \left\lbrack {0,1} \right\rbrack},}\end{matrix} \\{{\beta_{ik}^{j,p} \in \left\lbrack {0,1} \right\rbrack},}\end{matrix} \\{{\beta_{ik}^{j,p} \notin \left\lbrack {0,1} \right\rbrack},}\end{matrix} \\{{\beta_{ik}^{j,p} \notin \left\lbrack {0,1} \right\rbrack},}\end{matrix} \\{{\beta_{ik}^{j,p} \in \left\lbrack {0,1} \right\rbrack},}\end{matrix} \\{{\beta_{ik}^{j,p} \in \left\lbrack {0,1} \right\rbrack},}\end{matrix} \\\;\end{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}{\beta_{il}^{j,p} \notin \left\lbrack {0,1} \right\rbrack} \\{\beta_{il}^{j,p} \in \left\lbrack {0,1} \right\rbrack}\end{matrix} \\{\beta_{il}^{j,p} \notin \left\lbrack {0,1} \right\rbrack}\end{matrix} \\{\beta_{il}^{j,p} \in \left\lbrack {0,1} \right\rbrack}\end{matrix} \\{\beta_{il}^{j,p} \notin \left\lbrack {0,1} \right\rbrack}\end{matrix} \\{\beta_{il}^{j,p} \in \left\lbrack {0,1} \right\rbrack}\end{matrix} \\{\beta_{il}^{j,p} \notin \left\lbrack {0,1} \right\rbrack}\end{matrix} \\{\beta_{il}^{j,p} \in \left\lbrack {0,1} \right\rbrack}\end{matrix} \\{\beta_{il}^{j,p} \notin \left\lbrack {0,1} \right\rbrack}\end{matrix} \\{\beta_{il}^{j,p} \in \left\lbrack {0,1} \right\rbrack}\end{matrix} \\{\beta_{il}^{j,p} \notin \left\lbrack {0,1} \right\rbrack}\end{matrix} \\{\beta_{il}^{j,p} \in \left\lbrack {0,1} \right\rbrack}\end{matrix} \\\;\end{matrix}} \right.} & (21)\end{matrix}$

Note that some of the listed cases may never occur. This is of lessconsequence in case the computations are implemented in a consecutiveway, following the order of presentation of this document.

Movement with Respect to Polygon Line Segments

The intersection between the line of movement as given by (12), and theline segment between r_(m) ^(p) and r_(n) ^(p), is given by the solutionto the following system of equations, which is solved with respect tothe parameters α_(i,mn) ^(p) and γ_(mn) ^(p), where the subscript referto the points involved in the computation

$\begin{matrix}\begin{matrix}{{{r_{i}^{p} + {\alpha_{i,{mn}}^{p}\left( {r_{CG} - r_{i}^{p}} \right)}} = {r_{m}^{p} + {\gamma_{mn}^{p}\left( {r_{n}^{p} - r_{m}^{p}} \right)}}}} \\{\left. \Leftrightarrow{\left( {\left( {r_{CG} - r_{i}^{p}} \right) - \left( {r_{n}^{p} - r_{m}^{p}} \right)} \right)\begin{pmatrix}\alpha_{i,{mn}}^{p} \\\gamma_{mn}^{p}\end{pmatrix}} \right. = {r_{m}^{p} - {r_{i}^{p}.}}}\end{matrix} & (22)\end{matrix}$

The solution shall not be computed for the points adjacent to r_(i)^(p). Furthermore, the intersection between the two lines fall outsidethe relevant line segment between r_(m) ^(p) and r_(n) ^(p) in caseγ_(mn) ^(p)∉[0,1]. If this is the case the intersection shall bedisregarded in the evaluation of the corner r_(i) ^(p). The requirementthat α_(i,mn) ^(p)>0 also remains. Note also that it is only needed tosolve (22) once for each corner point and iteration step of thealgorithm.

To obtain the complete picture, (22) is first solved for all linesegments, excluding the ones that are adjacent to r_(i) ^(p). Thesolution with the minimum value of α_(i,mn) ^(p), such that α_(i,mn)^(p)>0 and γ_(mn) ^(p)∈[0,1], is expressed as (note that since themovement is inward such a solution always exists)

α_(i,m) ₀ _(n) ₀ ^(p),γ_(m) ₀ _(n) ₀ ^(p)  (23)

Combination

Since, all high-precision measurement points are evaluated along thesame direction as far as constraints are concerned, they can be directlycombined. Note also that since one point is to be removed from theinterior of the polygon for each iteration step, the limitinghigh-precision measurement point is to be selected as the second onethat becomes active. The high-precision measurement point that becomesan active constraint is hence given by (24), where (24) can becalculated as follows

$\begin{matrix}{{j_{first} = {\underset{\underset{t_{j}^{m,p} \in P^{p}}{j}}{argmin}\; \alpha_{i}^{j,p}}}{j_{activeConstraint} = {\underset{\underset{r_{j}^{m,p} \in P^{p}}{j \neq j_{firts}}}{argmin}\; \alpha_{i}^{j,p}}}} & (24)\end{matrix}$

The corresponding movement becomes

α_(i) ^(p,measurementConstraints)=α_(i) ^(j) ^(activeConstraint)^(,p).  (25)

The result (25) is finally combined with the constraint imposed by thepossibility of self-intersection

α_(i) ^(p,allConstraints)=min(α_(i) ^(p,measurementConstraints),α_(l,m)₀ _(n) ₀ ^(p))−∈,  (26)

where ∈ is a small number that prevents that the constraint becomesexactly active, so that the search is started outside the constrainingpoint in the next iteration step.

Obtained Polygon Area Reduction

The obtained are reduction follows by integration, or equivalently,computation of the areas under the parts of the polygon shown in FIG. 9.

By consideration of the facts that the area under the curve can becomputed as sums of areas of rectangles and triangles, it is only theareas related to the moving and adjacent points that are affected by themovement, it follows that the areas before and after movement can beexpressed as:

$\begin{matrix}{A_{i,{before}}^{p} = {A_{0} + {\frac{1}{2}\left( {x_{i}^{p} - x_{k}^{p}} \right)\left( {y_{k}^{p} + y_{i}^{p}} \right)} + {\frac{1}{2}\left( {x_{l}^{p} - x_{i}^{p}} \right)\left( {y_{i}^{p} + y_{l}^{p}} \right)}}} & (27) \\{A_{i,{after}}^{p} = {A_{0} + {\frac{1}{2}\left( {{x_{i}^{p}\left( \alpha_{i}^{p,{allConstraints}} \right)} - x_{k}^{p}} \right)\left( {y_{k}^{p} + {y_{i}^{p}\left( \alpha_{i}^{p,{allConstraints}} \right)}} \right)} + {\frac{1}{2}\left( {x_{l}^{p} - {x_{l}^{p}\left( \alpha_{i}^{p,{allConstraints}} \right)}} \right){\left( {{y_{i}^{p}\left( \alpha_{i}^{p,{allConstraints}} \right)} + y_{l}^{p}} \right).}}}} & (28)\end{matrix}$

The reduction of area obtained is hence given by

$\begin{matrix}{{\Delta \; A_{i}^{p,{allConstraints}}} = {{{{\frac{1}{2}\left( {x_{l}^{p} - x_{k}^{p}} \right)\left( {y_{k}^{p} + y_{i}^{p}} \right)} + {\frac{1}{2}\left( {x_{l}^{p} - x_{i}^{p}} \right)\left( {y_{i}^{p} + y_{l}^{p}} \right)} - {\frac{1}{2}\left( {{x_{i}^{p}\left( \alpha_{i}^{p,{allConstraints}} \right)} - x_{k}^{p}} \right)\left( {y_{k}^{p} + {y_{l}^{p}\left( \alpha_{l}^{p,{allConstraints}} \right)}} \right)} - {\frac{1}{2}\left( {x_{l}^{p} - {x_{i}^{p}\left( \alpha_{l}^{p,{allConstraints}} \right)}} \right)\left( {{y_{i}^{p}\left( \alpha_{i}^{p,{allConstraints}} \right)} + y_{l}^{p}} \right)}}}.}} & (29)\end{matrix}$

The maximum of this area reduction measure determines which of the N_(p)corners to move at a specific iteration, whereas (12) and (26) determinethe movement.

The Algorithm

In the algorithm below N_(p) ^(m,rem) denotes the number ofhigh-precision measurement points that remain in the interior of thepolygon, at each corner movement iteration step. The algorithm forpolygon computation, for one specific cell relation configuration p isthen:

Initialization:

-   -   Compute the centre of gravity of all high-precision measurements        of the cluster (6).    -   Compute the maximum distance r from the centre of gravity (7),        (8).    -   Compute the initial polygon distributed around the circle R (9),        (10), (11).

Area Minimization:

Repeat until N_(p) ^(m,rem)<C^(p)N_(p) ^(m) or α_(i)^(p,allConstraints)≦0 (Measurement removal loop).

-   -   Compute the centre of gravity for the points that remain in the        interior of the polygon (6).    -   For i=1 to N_(p) (Corner movement evaluation loop).        -   For j=1 to N_(p) ^(m,rem) (Measurement point constraint            evaluation loop).            -   Compute and store allowed, point-wise constrained,                corner movement (21).        -   End (Measurement point constraint evaluation loop).        -   Compute and store allowed combined, measurement constrained,            movement (24), (25).        -   Compute and store allowed, self-intersection constrained,            movement (23).        -   Compute and store combined allowed, measurement and            self-intersection constrained, movement (26).        -   Compute and store area reduction (29), corresponding to            (26).

End (Corner movement evaluation loop).

-   -   Find the corner with index i₀ corresponding to the maximum area        reduction.    -   Update (12) the corner i₀ with the movement α_(i) ₀        ^(p,allConstraints).    -   Remove the high-precision measurement point that is no longer in        the interior of the polygon, from any lists of interior points.

N _(p) ^(m,rem) :=N _(p) ^(m,rem)−1.

End (Measurement removal loop).

Transform the final corner points of the polygon to WGS 84c latitudesand longitudes.

NUMERICAL EXAMPLE

Since the clustering algorithm is relatively simple, an example showingthe operation of the polygon shrinking algorithm is shown in FIGS.10A-B. In the example 3000 high-precision measurement points weregenerated, according to the figure. As can be seen there are threeoverlapping “hot spots” in a v-shaped configuration around which themeasurements are clustered. A 15 corner polygon, initiated according toFIG. 10A was optimized using a prescribed confidence of 95%. The resultis excellent, see FIG. 10B.

1. A method for providing position determination assisting data in acellular communications network, comprising the steps of: establishing acell relation configuration for a user equipment; said cell relationconfiguration comprising at least cell identities of cells, in whichsignals to/from said user equipment fulfil at least a specific radiocondition criterion when received; performing a high-precision positiondetermination for said user equipment; repeating said establishing andperforming steps a plurality of times; clustering results of saidhigh-precision position determinations belonging to the same cellrelation configuration in separate clustered results; associating anarea definition with at least one of said clustered results; creatingposition determination assisting data comprising a relation between saidcell relation configurations and said associated area definitions. 2.The method according to claim 1, wherein said specific radio conditioncriterion is that the connection is used in at least one of softhandover and softer handover.
 3. The method according to claim 1,wherein said specific radio condition criterion is that the signalenables identification of the cell of the transmitting/receiving node.4. The method according to claim 1, wherein said cell relationconfiguration further comprises an ordering of the comprised cellidentities.
 5. The method according to claim 4, wherein said ordering isassociated with a signal-strength related quantity.
 6. The methodaccording to claim 5, wherein said signal-strength related quantity isselected from the list of: signal strength; path loss; andsignal-to-interference ratio.
 7. The method according to claim 1,wherein said associated area contains a predetermined percentage of saidclustered results.
 8. The method according to claim 7, wherein an areameasure of said associated area definition is minimized.
 9. The methodaccording to claim 8, wherein said associated area definition is apolygon.
 10. The method according to any of the claims 1-7, wherein saidassociated area definition is a polygon.
 11. The method according toclaim 10, wherein said associating step in turn comprises the steps of:encompassing at least one of said clustered results of saidhigh-precision position determinations, belonging to one cell relationconfiguration by a polygon; altering the position of corners of saidpolygon along predetermined paths to improve a predetermined criterionwhile maintaining at least a predetermined percentage of saidhigh-precision position determinations of the cluster within thepolygon.
 12. The method according to claim 11, wherein said improvementis an optimization of the present altering step.
 13. The methodaccording to claim 11, wherein said step of altering is repeated untilanother altering step would invalidate said predetermined percentage ofhigh-precision position determinations of the clustered results withinthe polygon.
 14. The method according to claim 11, wherein saidpredetermined criterion is an as large area reduction of said polygon aspossible.
 15. The method according to claim 11, wherein saidpredetermined criterion is an as large distance reduction as possiblebetween the centre of gravity of all high-precision positiondeterminations within the area and said altered corner.
 16. The methodaccording to claim 11, wherein said predetermined path is a curvethrough the original corner position and a centre of gravity for saidhigh-precision position determinations of the clustered results withinthe polygon.
 17. The method according to claim 16, wherein said curve isa straight line through the original corner position and a centre ofgravity for said high-precision position determinations of the clusteredresults within the polygon.
 18. The method according to claim 11,wherein said altering step comprises altering of one corner position ata time, allowing one of said high-precision position determinations ofsaid clustered results to be placed outside said polygon, but not two ofsaid high-precision position determinations.
 19. The method according toclaim 11, wherein more than one of said high-precision positiondeterminations of said clustered results are allowed to be placedoutside said polygon at least one of said altering steps.
 20. The methodaccording to claim 18, wherein said altering alters one corner positionat a time and brings one of said high-precision measurements of saidclustered results to be placed on a linear segment between the cornerthat is altered and a neighbouring corner.
 21. The method according toclaim 11, wherein, in said altering step, the position of said cornersof said polygon is tentatively altered along more than one predeterminedpath and said predetermined path being selected as the path giving thebest results according to said predetermined criterion.
 22. The methodaccording to claim 1, wherein, in said clustering step, said results ofsaid high-precision position determinations to be clustered are selectedaccording to a further criterion.
 23. The method according to claim 22,wherein said further criterion is based on at least one of auxiliaryinformation about circumstances of signalling and auxiliary measurementsof signalling properties.
 24. The method according to claim 23, furthercomprising the step of recording a measuring instant of saidhigh-precision position determinations, whereby said further criterionis based on at least said measuring instant.
 25. The method according toclaim 24, wherein only results of said high-precision positiondeterminations younger than a predetermined age are clustered.
 26. Themethod according to claim 24, wherein only results of saidhigh-precision position determinations being measured during one orseveral predetermined time periods of a day, week or year are clustered.27. The method according to claim 23, further comprising the step ofrecording a type of radio access bearer used during said high-precisionposition determinations, whereby said further criterion is based on atleast said type of radio access bearer.
 28. The method according toclaim 23, further comprising the step of recording a round trip time fora radio signal with respect to a particular base station, whereby saidfurther criterion is based on at least said round trip time.
 29. Themethod according to claim 1, wherein said clustering, associating andcreating steps are performed continuously or intermittently.
 30. Themethod according to claim 29, wherein said clustering, associating andcreating steps are performed for at least one of possible cell relationconfigurations.
 31. The method according to claim 30, further comprisingthe step of storing the last achieved position determination assistingdata at a computer readable medium.
 32. The method according to claim 1,wherein said clustering, associating and creating steps are performedwhen a position determination is requested.
 33. A method for radionetwork planning, comprising the steps of: obtaining positiondetermination assisting data provided according to claim 1; said step ofperforming a high-precision position determination being performed ondemand; and evaluating said position determination assisting dataregarding actual radio propagation.
 34. A method for determining aposition of a user equipment in a cellular communications network,comprising the steps of: obtaining position determination assisting dataprovided according to claim 1; establishing a cell relationconfiguration for said user equipment; said cell relation configurationcomprising at least cell identities of cells, in which signals to/fromsaid user equipment fulfil at least a specific radio condition criterionwhen received; and determining, by said position determination assistingdata, an area definition related to said cell relation configuration asdefining an area in which said user equipment is positioned.
 35. Themethod according to claim 34, further comprising the step of: providingat least one of auxiliary information about circumstances of signallingand auxiliary measurements of signalling properties; whereby said stepof determining an area definition is based also on said at least one ofauxiliary information about circumstances of signalling and auxiliarymeasurements of signalling properties.
 36. The method according to claim34, wherein said area definition is a polygon.
 37. A method fordetermining a position of a user equipment in a cellular communicationsnetwork, comprising the steps of: determining an initial position ofsaid user equipment according to claim 34; and refining said initialposition by a refined positioning method.
 38. The method according toclaim 37, wherein said refined positioning method is based on UTDOAmeasurements.
 39. The method according to claim 37, wherein said refinedpositioning method is based on RTT measurements.
 40. The methodaccording to claim 37, wherein said refined positioning method is basedon assisted GPS.
 41. An arrangement for providing position determinationassisting data in a cellular communications network, comprising: meansfor establishing a cell relation configuration for a user equipment;said cell relation configuration comprising at least cell identities ofcells, in which signals to/from said user equipment fulfil at least aspecific radio condition criterion when received; means for performing ahigh-precision position determination for said user equipment; means forclustering results of said high-precision position determinationsbelonging to the same cell relation configuration in separate clusteredresults; and means for associating an area definition with at least oneof said clustered results and creating position determination assistingdata comprising a relation between said cell relation configurations andsaid associated area definitions.
 42. An arrangement for determining aposition of a user equipment in a cellular communications network,comprising: arrangement for obtaining position determination assistingdata according to claim 41; means for establishing a cell relationconfiguration for said user equipment; said cell relation configurationcomprising at least cell identities of cells, in which signals to/fromsaid user equipment fulfil at least a specific radio condition criterionwhen received; and means for determining, by said position determinationassisting data, an area definition related to said cell relationconfiguration as defining an area in which said user equipment ispositioned.
 43. The arrangement according to claim 42, furthercomprising: means for refining said area in which said user equipment ispositioned by a refined positioning method.
 44. The arrangementaccording to claim 43, wherein said means for refining comprises meansfor performing a UTDOA positioning.
 45. The arrangement according toclaim 43, wherein said means for refining comprises means for performinga KIT positioning.
 46. The arrangement according to claim 43, whereinsaid means for refining comprises assisted GPS means.
 47. A node of acellular communications network, comprising an arrangement according toclaim
 42. 48. The node according to claim 47, being a node selected fromthe list of: base station; base station controller; radio networkcontroller; service mobile location centre; and stand alone servicemobile location centre.
 49. A cellular communications network,comprising an arrangement according to claim
 42. 50. A computer readablemedium comprising position determination assisting data providedaccording to claim 1.